The development of autonomous vehicles has progressed significantly due to the expansion in perception, motion planning and control, and/or emerging sensing technologies, among other factors. To achieve autonomous navigation, accurate localization and mapping may be needed. Autonomous vehicles may capture images and point clouds of an environment to assist in the localization and mapping. Autonomous vehicles perform Simultaneous Localization And Mapping (“SLAM”) operations on the captured images and point clouds to build a map of the environment and obtain motion and trajectory/odometry data. SLAM operations may include one or more operations to extract, associate, estimate, and/or update localization and mapping. Further, autonomous vehicles also perform semantic mapping and scene understanding techniques. Frequently, additional points may be projected onto images during the semantic mapping and scene understanding due to a misalignment of LIDAR and camera placement, and/or due to sensor noise in the LIDAR. Inclusion of these additional points may be a problem for accurate semantic mapping and scene understanding for autonomous vehicles.
In view of the foregoing, there may be a need in the art for ways to more accurately perform semantic mapping by filtering out the additional points generated by the LIDAR based on noise/errors. Further advantages and novel features will become apparent from the disclosure provided below.